MongoDB vs Amazon s3 is one popular search among developers and startups but this should not be the question to ask because they solve different needs in the in today's modern applications.
Yes! they both store data but it is imperative to note that one is a 'Database' and the other is a 'Cloud Storage'.
In this article, we will take a closer look at how the two storage services differ based on their various usecases, mode of data storage among others.
What is MongoDB?
MongoDB is a document database with the scalability and flexibility that you want with the querying and indexing that you need.
MongoDB is a document database, which means it stores data in JSON-like documents.
We call the format BSON, which extends JSON to add support for binary data.
It is classified as a NoSQL database / Schemaless as well as a cloud storage service. NoSQL databases are designed to handle large amounts of data quickly and easily. MongoDB is an open-source tool.
What is Amazon S3?
Amazon Simple Storage Service (Amazon S3) is an object storage service that provides industry-leading scalability, Data availability, security, and performance.
It comes with a simple web services interface to store and retrieve any amount of data from anywhere on the web and very easy to use.
It is designed to make web-scale computing easier for developers. It is classified as a cloud storage service.
Amazon S3 is designed for 99.999999999% (11 9’s) of durability, and stores data for millions of applications for companies all around the world.
MongoDB vs Amazon S3
Now that we have a basic understanding of what MongoDB and Amazon S3 are, let's compare and contrast them.
Which one is better for your use case? How do I choose between them? Let's find out.
In this article, we will compare MongoDB vs Amazon S3 in terms of the following:
- High Performance
- Data Storage
- Scalability
- Cost
- Data availability
✔ High Performance
| MongoDB | Amazon S3 |
|---|---|
| The NoSQL nature of MongoDB makes data operations quick and easy. Without compromising the data integrity, data can be quickly stored, updated and retrieved. | AWS S3 provides actions such as multi-Part payload which gives low latency of 100–200 milliseconds which automatically grows to high request rates. |
✔ Data Storage
| MongoDB | Amazon S3 |
|---|---|
| MongoDB is a document database that stores data in JSON-like documents. | Amazon S3 is an object storage service that stores data as objects within buckets. |
| It stores data as documents. | It stores data as objects. |
| Data is stored in BSON format. | Data is stored in binary format. |
| MongoDB stores data in collections. | Amazon S3 stores data in buckets. |
| MongoDB stores data in databases. | Amazon S3 stores data in regions. |
| MongoDB is a NoSQL database / Schemaless as well as a cloud storage service | Amazon S3 is a cloud storage service |
✔ Scalability
| MongoDB | Amazon S3 |
|---|---|
| As the data grows, MongoDB quickly and equally distributed the data across a cluster of computers. This process is called "Sharding". Growing amounts of data are easily handled with MongoDB's scalability. | There is no set limit on the volume of data or the number of objects that can be stored in an S3 bucket. S3 offers infinite scalability. A bucket created in s3 has a 5T storage limit |
✔ Cost
In terms of cost, MongoDB is a free and open-source tool. It is free to use and has no limitations on the number of databases, collections, or documents that you can create. Amazon S3 is a paid service. It has a free tier that allows you to store up to 5 GB of data for 12 months. After that, you will be charged for the storage you use. The cost of Amazon S3 depends on the storage class you choose. The storage class you choose determines the cost of storage, data retrieval, and data transfer. The storage class you choose also determines the durability, availability, and performance of your data.
✔ Data Availability
| MongoDB | Amazon S3 |
|---|---|
| MongoDB duplicates data across several servers. This makes data available to be accessed in case any sever fails without any delay. | Amazon S3 is a cloud-based database. It is available on the cloud. |
Use Cases for MongoDB and Amazon S3
MongoDB
Primarily, MongoDB is utilized for huge data, or, to put it another way, that is the most common application for big data storage. This makes it a perfect fit for applications that require large volumes of data. It is used for applications in the field of Content management, Customer analytics, Real-time data integration, Internet of Things (IoT), and more.
Due to the diverse nature of MongoDB, it is used in some real-life application like:
- Forbes
- MetLife
- Aadhar
among others.
Amazon S3
The two most common use cases for Amazon S3 are:
- Time efficiency
- Centralization.
If you have, say, 10 million records, you can save a lot of time by attempting to extract selected data. Imagine that one user only needs 10 records from this file, while another user needs 1000 records. The first user utilizes S3 Select to discover 10 records only, as opposed to downloading 100 million records, and the second user only downloads 1000 records. None of this requires downloading all the 10 million records.
AWS S3 is mostly recommended for use in modern apps like social networking, gaming, Storage backup, and more.
Some popular real-life applications of Amazon S3 are:
- Netflix
- Airbnb
- McDonalds
- NASA
among others.
CONCLUSION
MongoDB is a Database whereas S3 is a generalized object store.
They are both great data storage services in their usecases. They both have their pros and cons.
They are not alternatives. S3 in general is an object store for Images, GIFs, documents and all kinds of files and MongoDB being a database is for a structured store of data.
References
Happy Hacking!
Have you used any of these services for your project?
You can share your experience in the comment section to help other developers who are yet to you them in their projects.
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Top comments (2)
In my experience, S3 does terribly when retrieving a large set of records. I retrieved 1000 records of a few Kb, I got 10 per seconds. The same on MongoDB took 0.1s, so that's 10000 per seconds. 1000 times faster.
Oh wow
Thank you for this input